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Data Processing Assistant for Resting-State fMRI (DPARSF) V2.3

Submitted by YAN Chao-Gan on Sun, 06/16/2013 - 07:48

Data Processing Assistant for Resting-State fMRI (DPARSF) is a convenient plug-in software based on SPM and REST. You just need to arrange your DICOM files, and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data, functional connectivity, ReHo, ALFF/fALFF, degree centrality, voxel-mirrored homotopic connectivity (VMHC) results. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. You can use DPARSF to extract ROI time courses efficiently if you want to perform small-world analysis. DPARSF basic edition is very easy to use while DPARSF advanced edition (alias: DPARSFA) is much more flexible and powerful. DPARSFA can parallel the computation for each subject, and can be used to reorient your images interactively or define regions of interest interactively. You can skip or combine the processing steps in DPARSF advanced edition freely. Please download a MULTIMEDIA COURSEto know more about how to use this software. Add DPARSF's directory to MATLAB's path and enter "DPARSF" or "DPARSFA" in the command window to enjoy DPARSF basic edition or advanced edition.

New features of DPARSF_V2.3_130615:1.Apply downloaded reorient matrices. Given the amount of time and effort for interactive reorienting, the reorient matrices for online data such as 1000 Functional Connectomes Project (FCP) (Biswal et al., 2010) and Autism Brain Imaging Data Exchange (ABIDE) (Di Martino et al., 2013) could be downloaded and applied automatically. Please find here for “DownloadedReorientMats”, and then tick the checkbox "Apply Mats".2.Save information of TR, Slice Number, Time Points and Voxel Size into TRInfo.tsv (under working directory) file for checking data correctness.3.The slice order type could be specified for each participant into SliceOrderInfo.tsv (under working directory) file, thus allow different slice timing correction for different participants in a batch mode. Please find instructions for setting SliceOrderInfo.tsv from {DPARSF}/Docs/SliceOrderInfo.tsv_Instruction.txt.4.The output format of DICOM to NIfTI were changed to 4D .nii images.5.The midline of VMHC results were set to zero.

I noticed a problem that the lastly defined ROI and it's ROI indices get lost when writing the ROI_OrderKey_*.csv files for the 'extract ROI signal' step. Could you please remove the following line at the bottom part of y_ExtractROISignal.m ?

I want to do a correlation analysis between FC-Maps of a ROI and a text variable (duration of illness). What I did so far:I use DPARSF --> statistical analysis --> Correlation analysis: Add FC-images of the patient-group (which are saved in the folder "FC-FunImgRWSDF) and add a text file with the respective duration of illness (in one column; I have 61 subjects, so 61 lines). The calculation works and I can open the result-file.The first thing I am wondering about is that when I add the group images (so the folder "FC-FunImgRWSDF") the program immediatly reads in the standardized zFCmaps as well as the FCmaps. However I guess that I only want the zFCmaps to be included in the calculation, right? Do I just have to put the zFCmaps in a seperate folder and then add this folder as the image-folder or is there another way (like in one-sample-t-test, where I just type in a 0 for standardized and a 1 for unstandardized data).Second, when I look at the result-image with a threshold of p=0.05 I see all significant (but uncorrected) correlations of the FCmap and the text-variable, am I right? If i now want to find out about the exact correlation of a respective cluster in this image, I can use the cluster-report. However this will only show me the correlation of the peak-voxel. Is there a way that the programm can tell me the overall correlation for the respective significant cluster?

Thank you very much for the response. Another question: When I do a one-sample or two-sample t-test, do I always have to put the zFC maps in an extra folder? I thought that in a one-sample-t-test by setting the base to 0 (instead of 1) the program would automatically use just the zFC-maps.

Thanks again :-)I have another problem: I want to take a look at the individual zFCmaps. It works for almost every subject. However for 5 subjects I get the following error message once I try to open the zFCmaps in DPABI-Viewer:

Error using repmat

Replication factors must be a row vector of integers or integer scalars.

Ok, the problem with viewing the single subject zFCmaps didn't occur again :-)

However I have another problem. I did a two-sample-t-Test of zFCmaps and now want to do an FDR-correction. I first opened DPABI-viewer, loaded up the T2-image and then just click in "FDR" and set the q-value to 0.05. Am I right?I did that and unfortunately no differences were found. However a colleaque of mine is using the same data but a different programm and somehow he finds a group difference after FDR-correction. So I was wondering whether I might do it wrong in DPARSF. Also, the other programm filters the z-FCmaps on a threshold of 0.4 before doing any calculations such as t-tests. Only those voxels which are higher than the threshold of 0.4 will be included in the t-test, is that possible in DPARSF as well?

Another question: What threshold for the T-Value would you recommend when looking at the seed-network of one group (using one-sample-test of zFCmaps). For the Default-Mode-Netork for instance and a group-size of N=60 I use a T-value of 8 and the network looks quite good, however I am wondering whether this is the proper threshold for checking network quality.

thanks for the reponse :-)It sounds very interesting, however, I am not sure whether I am doing it right. How do I create a mask upon the significant voxels of both one-sample-t-tests? I tried uploaing both t-test images (of patients and controls) in the DPABI-Viewer, then I set the threshold to 0.000001 and then clicked on save all clusters. This will produce a mask which I then uploaded as a mask when doing the FDR-Correction. Is that the procedure you were talking about or should I do it differently?

Also, which parameters should I use in a GRF-correction. Would you recommend both p=0.05 in Voxel and cluster p-value?

When I load up the images in the image calculator, do I have to add anything under "Group images"?I then do FDR correction with the two-sample-t-test and load up the mask in the FDR-correction-window, is that right?

Also, when doing GRF correction, what p-value for cluster respectively voxel would you recommend when using it in a one-sample-t-test to check for network quality. Do I have to load up a mask there as well?

Ok, thanks that was helpful :-)When I view the one-sample-t-test for a network of one group and set the threshold to p<0.000001 should I do an additional FDR correction or is that unnecessary since the p-value is that small?

Also, when I do an GRF-correction on a one-sample-t-test to check network quality, do I have to upload a mask as well?